Weekly Exercises #3

library(tidyverse)     # for graphing and data cleaning
library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation
library(ggthemes)      # for even more plotting themes
library(geofacet)      # for special faceting with US map layout
theme_set(theme_minimal())       # My favorite ggplot() theme :)

# Lisa's garden data
data("garden_harvest")

# Seeds/plants (and other garden supply) costs
data("garden_spending")

# Planting dates and locations
data("garden_planting")

# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')

Setting up on GitHub!

Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 3rd weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises with garden data

These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

1. Summarize the garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.

garden_harvest %>% 
  mutate(obs = row_number(),
         weekday = wday(date, label = TRUE)) %>% 
  group_by(vegetable, weekday) %>% 
  summarize(tot_wt_dow = (sum(weight)* 0.00220462)) %>% 
  pivot_wider(names_from = weekday,
              values_from = tot_wt_dow)

2. Summarize the garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?

garden_harvest %>% 
  group_by(variety, vegetable) %>% 
  summarize(total_variety_wt_lbs = (sum(weight)* 0.00220462))

garden_harvest %>% 
  left_join(garden_planting,
            by = c("vegetable", "variety")) %>% 
  group_by(variety, vegetable, plot) %>% 
  summarize(total_variety_wt_lbs = (sum(weight)* 0.00220462))

Discussion: The issue is that there are certain varieties of vegetables that have been planted in multiple plots, or were not recorded in a plot. As such, we see in the table that the instances in which a variety has been planted in multiple plots will appear as many times as places it has been planted. In these instances, the total harvest weight of that variety will appear as many times as places it has been planted, which might lead someone to believe that the total weight is multipilcatively more than it actually is. To fix this issue, it likely would’ve required the data entry to differentiate the varieties by the plot they’re in if the variety was planted in more than one plot. This would’ve allowed the grouping to differentiate between the plots and we could’ve calculated totals by variety and plot.

3. I would like to understand how much money I “saved” by gardening, for each vegetable type. Describe how I could use the garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.

Discussion: To gain an understanding of how much money you saved by gardening, it would be helpful to understand how much the total weight of each vegetable and variety would cost at a grocery store. Using a grocery store’s website, you could calculate the total cost by multiplying their price per lb by the number of lbs you harvested in your garden. You could create a dataset with the grocery store prices and join that data set using a left_join() by vegetable/variety (assuming that you could find all varieties and vegetables at the grocery store). You could create a further complete dataset to help understand your savings by left joining that dataset with the garden_spending dataset. You could then, using all this information, create a variable that calculates the cost for each vegetable/variety at a grocery store, and then create another variable that shows the savings by subtracting your initial investment in the seeds/plant from the total grocery store cost to show an estimated savings.

4. Subset the data to tomatoes. Reorder the tomato varieties from smallest to largest first harvest date. Create a barplot of total harvest in pounds for each variety, in the new order.

garden_harvest %>% 
  filter(vegetable == "tomatoes") %>% 
  group_by(variety) %>% 
  summarize(first_harvest = min(date), 
            tot_wt = (sum(weight)*0.00220462)) %>% 
  arrange(first_harvest)

garden_harvest %>% 
  filter(vegetable == "tomatoes") %>% 
  group_by(variety) %>% 
  summarize(first_harvest = min(date), 
            tot_wt = (sum(weight)*0.00220462)) %>% 
  ggplot(aes(y = fct_reorder(variety, first_harvest, min), x = tot_wt)) +
  geom_col() +
  labs(title = "Total Tomato Harvest in lbs, Sorted by Weight of First Harvest", 
       x = "", 
       y = "")

5. In the garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().

garden_harvest %>% 
  distinct(variety, .keep_all = TRUE) %>% 
  group_by(variety) %>% 
  mutate(variety_lower = str_to_lower(variety),
         variety_length = str_length(variety)) %>%
  group_by(vegetable) %>% 
  arrange(variety_length) %>% 
  summarize(vegetable, 
            variety_lower, 
            variety_length)

6. In the garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().

garden_harvest %>% 
  distinct(variety, .keep_all = TRUE) %>% 
  mutate(has_er_ar = str_detect(variety, "er|ar")) 

Bicycle-Use Patterns

In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.

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Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.

Temporal patterns

It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:

7. A density plot, which is a smoothed out histogram, of the events versus sdate. Use geom_density().

Trips %>% 
  ggplot(aes(x = sdate)) + 
  geom_density() +
  labs(title = "Distribution of Bike Rentals Between October and January 2014", 
       x = "", 
       y = "Density")

8. A density plot of the events versus time of day. You can use mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.

Trips %>% 
  mutate(hour = hour(sdate), 
         minute = minute(sdate), 
         time = hour + (minute/60)) %>% 
  ggplot(aes(x = time)) +
  geom_density() +
  labs(title = "Distribution of Bike Rental Times", 
       x = "Time of Day (Military Time)", 
       y = "Density") +
  theme_minimal()

9. A bar graph of the events versus day of the week. Put day on the y-axis.

Trips %>% 
  mutate(weekday = wday(sdate, label = TRUE)) %>% 
  ggplot(aes(y = weekday)) + 
  geom_bar() +
  labs(title = "Distribution of Bike Rentals by Day of the Week", 
       x = "Number of Rentals", 
       y = "")

10. Facet your graph from exercise 8. by day of the week. Is there a pattern?

Trips %>% 
  mutate(weekday = wday(sdate, label = TRUE), 
         hour = hour(sdate), 
         minute = minute(sdate), 
         time = hour + (minute/60)) %>% 
  ggplot(aes(x = time)) +
  geom_density() +
  facet_wrap(vars(weekday)) +
  labs(title = "Distribution of Bike Rentals by Time of Day and Weekday", 
       x = "Time of Day (Military Time)", 
       y = "Density")

Observations: There is a distinct pattern present. On weekdays, as opposed to weekends, there are two distinct peaks in rental occurrences that coincide with the start and of the workday. These peaks are absent on the weekends where there is more of a standard, unimodal distribution that peaks a little after noon.

The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.

11. Change the graph from exercise 10 to set the fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.

Trips %>% 
  mutate(weekday = wday(sdate, label = TRUE), 
         hour = hour(sdate), 
         minute = minute(sdate), 
         time = hour + (minute/60)) %>% 
  ggplot(aes(x = time)) +
  geom_density(aes(fill = client), alpha = .5, color = NA) +
  facet_wrap(vars(weekday)) +
  labs(title = "Distribution of Bike Rentals by Time of Day and Weekday",  
       x = "Time of Day (Military Time)", 
       y = "Density", 
       color = "", 
       fill = "Client Type")

12. Change the previous graph by adding the argument position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?

Trips %>% 
  mutate(weekday = wday(sdate, label = TRUE), 
         hour = hour(sdate), 
         minute = minute(sdate), 
         time = hour + (minute/60)) %>% 
  ggplot(aes(x = time)) +
  geom_density(aes(fill = client), alpha = .5, color = NA, position = position_stack()) +
  facet_wrap(vars(weekday)) +
  labs(title = "Distribution of Bike Rentals by Time of Day and Weekday",  
       x = "Time of Day (Military Time)", 
       y = "Density", 
       fill = "Client Type")

Discussion: Personally, I find this format to better at telling certain aspects of the story and worse at telling other parts. For instance, I believe that stacking these plots makes it more difficult to visually separate the trends for the casual and registered riders, even despite these trends still being visible in the stacked plots. This might just be personal preference. The stacking does help, however, in gauging the relative proportions of client types among riders and we can more directly compare the proportions of riders by time and day of week in the stacked version. So, the stacked version definitely has its strengths, but that’s not to say it’s without its weaknesses too. For instance, I find that its less intuitive to read the stacked version. Without knowing exactly what the stacking is representing and how to read it, it’s less accessible than the alternative. Ultimately, I think both are effective story telling devices and it probably depends more on individual taste and what you’re used to seeing.

13. In this graph, go back to using the regular density plot (without position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.

Trips %>% 
  mutate(weekday = wday(sdate, label = FALSE), 
         hour = hour(sdate), 
         minute = minute(sdate), 
         time = hour + (minute/60), 
         weekend = ifelse(weekday > 5, "Weekend", "Weekday")) %>% 
  ggplot(aes(x = time)) +
  geom_density(aes(fill = client), alpha = .5, color = NA) +
  facet_wrap(vars(weekend)) + 
   labs(title = "Weekday vs Weekend: Distribution of Bike Rentals by Time of Day",  
       x = "Time of Day (Military Time)", 
       y = "Density", 
       fill = "Client Type")

14. Change the graph from the previous problem to facet on client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?

Trips %>% 
  mutate(weekday = wday(sdate, label = TRUE), 
         hour = hour(sdate), 
         minute = minute(sdate), 
         time = hour + (minute/60), 
         weekend = ifelse(weekday > 5, "Weekend", "Weekday")) %>% 
  ggplot(aes(x = time)) +
  geom_density(aes(fill = weekday), alpha = .5, color = NA) +
  facet_wrap(vars(client)) + 
  labs(title = "Weekday vs Weekend: Distribution of Bike Rentals by Time of Day",  
       x = "Time of Day (Military Time)", 
       y = "Density", 
       fill = "Client Type")

Discussion: This graph supposedly helps provide more nuanced information about the specific days of the week, which was absent from the previous graph. Essentially, we can see if certain times of the day are more popular for certain days of the week and overlay them to create a tapestry of the full week. Personally, I don’t believe this graph provides much better information than the previous graph, and I believe this information would be better presented in a faceted form. Perhaps if the color scheme was better I would have more affinity towards it, but its current form is, in my opinion, somewhat needless.

Spatial patterns

15. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!

Trips %>% 
  group_by(sstation) %>% 
  summarize(n_departures = n()) %>% 
  arrange(desc(n_departures)) %>% 
  left_join(Stations, 
            by = c("sstation"="name")) %>% 
  ggplot(aes(y = lat, x = long, color = n_departures)) +
  geom_point() + 
  scale_color_gradient(low="blue", high="red") +
  labs(title = "Spatial Heatmap of Station Popularity", 
       x = "Longitude", 
       y = "Latitude", 
       subtitle = "Total Number of Departures at Each Station", 
       color = "Departures")

16. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? (Again, we’ll improve this next week when we learn about maps).

Trips %>% 
  group_by(sstation) %>%
  mutate(casual = client == "Casual", 
         registered = client == "Registered") %>% 
  group_by(sstation) %>% 
  summarize(prop_casual = sum(casual)/(sum(registered) + sum(casual))) %>% 
  arrange(desc(prop_casual)) %>% 
  left_join(Stations, 
            by = c("sstation"="name")) %>% 
  ggplot(aes(y = lat, x = long, color = prop_casual)) +
  geom_point() + 
  scale_color_gradient(low="blue", high="red") +
  labs(title = "Spatial Heatmap of Most Popular Stations for Casual Clients", 
       x = "Longitude", 
       y = "Latitude", 
       color = "Percent Casual", 
       subtitle = "Proportion of Total Rentals by Causal Clients at Each Station")

Observations: It seems that most stations are used mostly by registered clients as most points are blue or purple. There are some clusters of stations with higher percentages of casual clients in the top left corner and in the center of the main cluster around 38.9 lat. It’s difficult to really denote any patterns beyond this, however.

Spatiotemporal patterns

17. Make a table with the ten station-date combinations (e.g., 14th & V St., 2014-10-14) with the highest number of departures, sorted from most departures to fewest. Save this to a new dataset and print out the dataset. Hint: as_date(sdate) converts sdate from date-time format to date format.

TopTenSD <- Trips %>% 
  mutate(date = as_date(sdate)) %>% 
  group_by(date, sstation) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  head(10)

TopTenSD

18. Use a join operation to make a table with only those trips whose departures match those top ten station-date combinations from the previous part.

New_Trip <- Trips %>% 
  mutate(sdate = as_date(sdate))
  
TopTenSD %>% 
  select(date, sstation) %>% 
  inner_join(New_Trip, 
             by = c("date" = "sdate", "sstation"))

19. Build on the code from the previous problem (ie. copy that code below and then %>% into the next step.) and group the trips by client type and day of the week (use the name, not the number). Find the proportion of trips by day within each client type (ie. the proportions for all 7 days within each client type add up to 1). Display your results so day of week is a column and there is a column for each client type. Interpret your results.

TopTenSD %>% 
  select(date, sstation) %>% 
  inner_join(New_Trip, 
             by = c("date" = "sdate", "sstation")) %>% 
  mutate(weekday = wday(date, label = TRUE), 
         casual = client == "Casual", 
         registered = client == "Registered") %>%
  group_by(weekday) %>% 
  summarize(percent_casual = sum(casual/(sum(casual)+sum(registered))), 
           percent_registered = sum(registered)/(sum(casual)+sum(registered)))

Interpretation: From the table above, we can see that the vast, vast majority of bike rentals during the weekdays are by registered clients. In fact, registered clients make up over 90% of all rentals every day of the work week. This pattern nearly completely flips, however, on the weekends. On Sat. and Sun., over 80% of all rentals are by casual clients. This makes sense as people who are using the bikes for commuting to work would be more likely to become registered clients, whereas people who are using the bikes on the weekends are likely tourists or people who don’t require the bikes as frequently.

GitHub link

20. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 03_exercises.Rmd, provide a link to the 03_exercises.md file, which is the one that will be most readable on GitHub.

Link: https://github.com/greynolds1121/Reynolds_Week_3_HW/blob/main/Reynolds_HW3.md

Challenge problem!

This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.

21. In this exercise, you are going to try to replicate the graph below, created by Georgios Karamanis. I’m sure you can find the exact code on GitHub somewhere, but DON’T DO THAT! You will only be graded for putting an effort into this problem. So, give it a try and see how far you can get without doing too much googling. HINT: use facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.

kids %>% 
  filter(variable == "lib") %>% 
  group_by(state, year) %>% 
  summarize(variable, 
            spending = inf_adj_perchild) %>% 
  ggplot(aes(x = year, y= spending)) + 
  geom_smooth(method = "lm", se = FALSE) +
  facet_geo(vars(state), scales="free")